code: def _compute_divisor_matrix(
      base_kernel,
      diag_shift,
      fixed_inputs,
      fixed_inputs_is_missing=None,
  ):
      """Compute the modified kernel with respect to the fixed inputs."""
      # Mask out inputs before computing with the kernel to ensure non-NaN
      # gradients.
      fixed_inputs = _maybe_mask_fixed_inputs(
          fixed_inputs,
          base_kernel.feature_ndims,
          fixed_inputs_is_missing,
      )
      divisor_matrix = base_kernel.matrix(fixed_inputs, fixed_inputs)
      if diag_shift is not None:
          diag_shift = tf.convert_to_tensor(diag_shift)
          broadcast_shape = distribution_util.get_broadcast_shape(
              divisor_matrix, diag_shift[..., tf.newaxis, tf.newaxis]
          )
          divisor_matrix = tf.broadcast_to(
              divisor_matrix, broadcast_shape
          )
          divisor_matrix = _add_diagonal_shift(
              divisor_matrix, diag_shift[..., tf.newaxis]
          )
      return util.mask_matrix(
          divisor_matrix, is_missing=fixed_inputs_is_missing
      )
  
imports: import tensorflow.compat.v2
  from tensorflow_probability.python.internal import distribution_util
  from tensorflow_probability.python.internal import dtype_util
  from tensorflow_probability.python.internal import nest_util
  from tensorflow_probability.python.internal import parameter_properties
  from tensorflow_probability.python.internal import tensor_util
  from tensorflow_probability.python.math.psd_kernels import positive_semidefinite_kernel
  from tensorflow_probability.python.math.psd_kernels.internal import util
label_desc: Use image node_id 4 for calling a global function with example usage: _compute_divisor_matrix(base_kernel, diag_shift, fixed_inputs, fixed_inputs_is_missing) and returns: util